MPN-RRT*:用于无人机的3D城市路径规划的新方法,集成深度学习和采样优化
Yue Zheng1, Ang Li1, Zihan Chen1
1Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了MPN-RRT*,这是一种新的框架,可以在3D城市环境中增强无人机 (UAV) 路径规划. 该方法显著减少了计算时间,并改善了有效自主导航的路径质量.
科学领域:
- 机器人技术和自主系统
- 人工智能的人工智能
- 计算几何学的计算几何学
背景情况:
- 无人驾驶飞行器 (UAV) 需要在复杂的3D城市环境中有效地规划路径.
- 传统的RRT*算法在复杂的环境中面临着计算挑战和低于最佳的路径.
研究的目的:
- 为无人机在3D城市环境中开发一个增强的路径规划框架 (MPN-RRT*).
- 与传统方法相比,提高计算效率,路径最佳性和轨迹平滑性.
主要方法:
- 整合运动规划网络 (MPNet) 与RRT*进行智能采样.
- 通过将3D城市地形切成2D迷宫表示来减少尺寸.
- 应用转移学习来调整预先训练的MPNet模型以简化地图.
主要成果:
- 在更简单的环境中,MPN-RRT*实现了47.8%的规划时间缩短和19.8%的路径缩短.
- 观察到更光滑的轨迹,平均加速度减少了91.2%.
- 在复杂的场景中,MPN-RRT*比RRT*减少了14.2%的飞行时间和13.9%的路径长度.
结论:
- 深度学习与基于采样的规划的整合显著提高了无人机导航.
- MPN-RRT*为高维环境中的实时自主系统提供了可扩展的解决方案.
- 数据驱动的方法可以有效地增强经典算法以提高性能.
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